Overview

Dataset statistics

Number of variables33
Number of observations724
Missing cells1457
Missing cells (%)6.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory186.8 KiB
Average record size in memory264.2 B

Variable types

Categorical27
Numeric6

Warnings

Corporate Sales Volume Actual has constant value "$0" Constant
Address has a high cardinality: 723 distinct values High cardinality
City has a high cardinality: 314 distinct values High cardinality
Neighborhood has a high cardinality: 349 distinct values High cardinality
Location Sales Volume Actual has a high cardinality: 344 distinct values High cardinality
Year Established is highly correlated with Years In DatabaseHigh correlation
Years In Database is highly correlated with Year EstablishedHigh correlation
Own or Lease is highly correlated with Corporate Sales Volume ActualHigh correlation
Rent Expenses is highly correlated with Corporate Sales Volume ActualHigh correlation
Telcom Expenses is highly correlated with Legal Expenses and 4 other fieldsHigh correlation
Management/Administration Expenses is highly correlated with Corporate Sales Volume ActualHigh correlation
County is highly correlated with Corporate Sales Volume Actual and 2 other fieldsHigh correlation
Contract Labor Expenses is highly correlated with Corporate Sales Volume Actual and 1 other fieldsHigh correlation
Office Supplies Expense is highly correlated with Purchase Print Expenses and 2 other fieldsHigh correlation
Advertising Expenses is highly correlated with Corporate Sales Volume ActualHigh correlation
Legal Expenses is highly correlated with Telcom Expenses and 3 other fieldsHigh correlation
Accounting Expenses is highly correlated with Corporate Sales Volume Actual and 1 other fieldsHigh correlation
Purchase Print Expenses is highly correlated with Office Supplies Expense and 2 other fieldsHigh correlation
Utilities Expenses is highly correlated with Corporate Sales Volume ActualHigh correlation
Corporate Sales Volume Actual is highly correlated with Own or Lease and 21 other fieldsHigh correlation
Payroll and Benefits Expenses is highly correlated with Telcom Expenses and 2 other fieldsHigh correlation
Company Name is highly correlated with Corporate Sales Volume ActualHigh correlation
Location Employee Size Range is highly correlated with Corporate Sales Volume ActualHigh correlation
State is highly correlated with County and 2 other fieldsHigh correlation
Location Sales Volume Range is highly correlated with Office Supplies Expense and 2 other fieldsHigh correlation
Insurance Expenses is highly correlated with Telcom Expenses and 3 other fieldsHigh correlation
Square Footage is highly correlated with Corporate Sales Volume ActualHigh correlation
Package Container Expense is highly correlated with Corporate Sales Volume ActualHigh correlation
Metro Area is highly correlated with County and 2 other fieldsHigh correlation
Computer Expenses is highly correlated with Telcom Expenses and 4 other fieldsHigh correlation
Neighborhood has 295 (40.7%) missing values Missing
ZIP Four has 46 (6.4%) missing values Missing
Year Established has 703 (97.1%) missing values Missing
Own or Lease has 379 (52.3%) missing values Missing
Address is uniformly distributed Uniform
Neighborhood is uniformly distributed Uniform

Reproduction

Analysis started2021-01-15 00:06:20.241819
Analysis finished2021-01-15 00:06:45.283980
Duration25.04 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

Advertising Expenses
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.8%
Missing2
Missing (%)0.3%
Memory size5.8 KiB
$50,000 to $100,000
469 
$20,000 to $50,000
190 
$100,000 to $250,000
 
45
$5,000 to $10,000
 
9
$10,000 to $20,000
 
8

Length

Max length20
Median length19
Mean length18.75900277
Min length16

Characters and Unicode

Total characters13544
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row$20,000 to $50,000
2nd row$50,000 to $100,000
3rd row$50,000 to $100,000
4th row$50,000 to $100,000
5th row$50,000 to $100,000
ValueCountFrequency (%)
$50,000 to $100,000469
64.8%
$20,000 to $50,000190
26.2%
$100,000 to $250,00045
 
6.2%
$5,000 to $10,0009
 
1.2%
$10,000 to $20,0008
 
1.1%
Less than $5,0001
 
0.1%
(Missing)2
 
0.3%
2021-01-15T00:06:45.746251image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:06:45.946625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to721
33.3%
50,000659
30.4%
100,000514
23.7%
20,000198
 
9.1%
250,00045
 
2.1%
10,00017
 
0.8%
5,00010
 
0.5%
less1
 
< 0.1%
than1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
06276
46.3%
1444
 
10.7%
$1443
 
10.7%
,1443
 
10.7%
t722
 
5.3%
o721
 
5.3%
5714
 
5.3%
1531
 
3.9%
2243
 
1.8%
s2
 
< 0.1%
Other values (5)5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7764
57.3%
Lowercase Letter1449
 
10.7%
Space Separator1444
 
10.7%
Currency Symbol1443
 
10.7%
Other Punctuation1443
 
10.7%
Uppercase Letter1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
t722
49.8%
o721
49.8%
s2
 
0.1%
e1
 
0.1%
h1
 
0.1%
a1
 
0.1%
n1
 
0.1%
ValueCountFrequency (%)
06276
80.8%
5714
 
9.2%
1531
 
6.8%
2243
 
3.1%
ValueCountFrequency (%)
$1443
100.0%
ValueCountFrequency (%)
,1443
100.0%
ValueCountFrequency (%)
1444
100.0%
ValueCountFrequency (%)
L1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12094
89.3%
Latin1450
 
10.7%

Most frequent character per script

ValueCountFrequency (%)
t722
49.8%
o721
49.7%
s2
 
0.1%
L1
 
0.1%
e1
 
0.1%
h1
 
0.1%
a1
 
0.1%
n1
 
0.1%
ValueCountFrequency (%)
06276
51.9%
1444
 
11.9%
$1443
 
11.9%
,1443
 
11.9%
5714
 
5.9%
1531
 
4.4%
2243
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13544
100.0%

Most frequent character per block

ValueCountFrequency (%)
06276
46.3%
1444
 
10.7%
$1443
 
10.7%
,1443
 
10.7%
t722
 
5.3%
o721
 
5.3%
5714
 
5.3%
1531
 
3.9%
2243
 
1.8%
s2
 
< 0.1%
Other values (5)5
 
< 0.1%

Accounting Expenses
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing3
Missing (%)0.4%
Memory size5.8 KiB
$5,000 to $10,000
486 
$2,500 to $5,000
128 
$10,000 to $25,000
88 
$1,000 to $2,500
 
11
$500 to $1,000
 
8

Length

Max length18
Median length17
Mean length16.89597781
Min length14

Characters and Unicode

Total characters12182
Distinct characters9
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$2,500 to $5,000
2nd row$5,000 to $10,000
3rd row$5,000 to $10,000
4th row$5,000 to $10,000
5th row$5,000 to $10,000
ValueCountFrequency (%)
$5,000 to $10,000486
67.1%
$2,500 to $5,000128
 
17.7%
$10,000 to $25,00088
 
12.2%
$1,000 to $2,50011
 
1.5%
$500 to $1,0008
 
1.1%
(Missing)3
 
0.4%
2021-01-15T00:06:46.972730image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:06:47.164040image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to721
33.3%
5,000614
28.4%
10,000574
26.5%
2,500139
 
6.4%
25,00088
 
4.1%
1,00019
 
0.9%
5008
 
0.4%

Most occurring characters

ValueCountFrequency (%)
04753
39.0%
$1442
 
11.8%
1442
 
11.8%
,1434
 
11.8%
5849
 
7.0%
t721
 
5.9%
o721
 
5.9%
1593
 
4.9%
2227
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6422
52.7%
Currency Symbol1442
 
11.8%
Space Separator1442
 
11.8%
Lowercase Letter1442
 
11.8%
Other Punctuation1434
 
11.8%

Most frequent character per category

ValueCountFrequency (%)
04753
74.0%
5849
 
13.2%
1593
 
9.2%
2227
 
3.5%
ValueCountFrequency (%)
t721
50.0%
o721
50.0%
ValueCountFrequency (%)
$1442
100.0%
ValueCountFrequency (%)
,1434
100.0%
ValueCountFrequency (%)
1442
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10740
88.2%
Latin1442
 
11.8%

Most frequent character per script

ValueCountFrequency (%)
04753
44.3%
$1442
 
13.4%
1442
 
13.4%
,1434
 
13.4%
5849
 
7.9%
1593
 
5.5%
2227
 
2.1%
ValueCountFrequency (%)
t721
50.0%
o721
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12182
100.0%

Most frequent character per block

ValueCountFrequency (%)
04753
39.0%
$1442
 
11.8%
1442
 
11.8%
,1434
 
11.8%
5849
 
7.0%
t721
 
5.9%
o721
 
5.9%
1593
 
4.9%
2227
 
1.9%

Address
Categorical

HIGH CARDINALITY
UNIFORM

Distinct723
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
806 W Merrick Rd
 
2
428 Grand St
 
1
1501 Main St
 
1
5407 Kings Plz
 
1
2880 NE 8th St
 
1
Other values (718)
718 

Length

Max length30
Median length16
Mean length16.52209945
Min length7

Characters and Unicode

Total characters11962
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique722 ?
Unique (%)99.7%

Sample

1st row265 W Oakland Park Blvd
2nd row326 Indian Trce
3rd row1020 Weston Rd
4th row9835 Okeechobee Blvd
5th row828 S Military Trl
ValueCountFrequency (%)
806 W Merrick Rd2
 
0.3%
428 Grand St1
 
0.1%
1501 Main St1
 
0.1%
5407 Kings Plz1
 
0.1%
2880 NE 8th St1
 
0.1%
460 Hialeah Dr1
 
0.1%
5121 5th Ave1
 
0.1%
1946 Wantagh Ave1
 
0.1%
7602 Flatlands Ave1
 
0.1%
1831 State Route 351
 
0.1%
Other values (713)713
98.5%
2021-01-15T00:06:47.874664image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ave184
 
7.0%
st135
 
5.1%
rd86
 
3.3%
blvd78
 
3.0%
w69
 
2.6%
s44
 
1.7%
route41
 
1.6%
hwy36
 
1.4%
state35
 
1.3%
34
 
1.3%
Other values (1071)1882
71.7%

Most occurring characters

ValueCountFrequency (%)
1900
 
15.9%
e667
 
5.6%
t581
 
4.9%
1580
 
4.8%
a476
 
4.0%
0429
 
3.6%
r356
 
3.0%
d343
 
2.9%
n327
 
2.7%
2326
 
2.7%
Other values (55)5977
50.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5213
43.6%
Decimal Number3078
25.7%
Space Separator1900
 
15.9%
Uppercase Letter1724
 
14.4%
Other Punctuation35
 
0.3%
Dash Punctuation12
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
S325
18.9%
A216
12.5%
R162
9.4%
W160
9.3%
B144
8.4%
H99
 
5.7%
N99
 
5.7%
M69
 
4.0%
C58
 
3.4%
P54
 
3.1%
Other values (16)338
19.6%
ValueCountFrequency (%)
e667
12.8%
t581
11.1%
a476
 
9.1%
r356
 
6.8%
d343
 
6.6%
n327
 
6.3%
o311
 
6.0%
l302
 
5.8%
i301
 
5.8%
v288
 
5.5%
Other values (15)1261
24.2%
ValueCountFrequency (%)
1580
18.8%
0429
13.9%
2326
10.6%
5313
10.2%
3310
10.1%
4254
8.3%
6236
7.7%
8219
 
7.1%
7218
 
7.1%
9193
 
6.3%
ValueCountFrequency (%)
#32
91.4%
&3
 
8.6%
ValueCountFrequency (%)
1900
100.0%
ValueCountFrequency (%)
-12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6937
58.0%
Common5025
42.0%

Most frequent character per script

ValueCountFrequency (%)
e667
 
9.6%
t581
 
8.4%
a476
 
6.9%
r356
 
5.1%
d343
 
4.9%
n327
 
4.7%
S325
 
4.7%
o311
 
4.5%
l302
 
4.4%
i301
 
4.3%
Other values (41)2948
42.5%
ValueCountFrequency (%)
1900
37.8%
1580
 
11.5%
0429
 
8.5%
2326
 
6.5%
5313
 
6.2%
3310
 
6.2%
4254
 
5.1%
6236
 
4.7%
8219
 
4.4%
7218
 
4.3%
Other values (4)240
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII11962
100.0%

Most frequent character per block

ValueCountFrequency (%)
1900
 
15.9%
e667
 
5.6%
t581
 
4.9%
1580
 
4.8%
a476
 
4.0%
0429
 
3.6%
r356
 
3.0%
d343
 
2.9%
n327
 
2.7%
2326
 
2.7%
Other values (55)5977
50.0%

City
Categorical

HIGH CARDINALITY

Distinct314
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
New York
52 
Brooklyn
 
50
Miami
 
50
Bronx
 
39
Hialeah
 
16
Other values (309)
517 

Length

Max length22
Median length8
Mean length9.23480663
Min length5

Characters and Unicode

Total characters6686
Distinct characters50
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique222 ?
Unique (%)30.7%

Sample

1st rowWilton Manors
2nd rowWeston
3rd rowWeston
4th rowWest Palm Beach
5th rowWest Palm Beach
ValueCountFrequency (%)
New York52
 
7.2%
Brooklyn50
 
6.9%
Miami50
 
6.9%
Bronx39
 
5.4%
Hialeah16
 
2.2%
Fort Lauderdale13
 
1.8%
West Palm Beach11
 
1.5%
Jamaica10
 
1.4%
Newark10
 
1.4%
Jersey City9
 
1.2%
Other values (304)464
64.1%
2021-01-15T00:06:48.476443image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
miami65
 
6.2%
new59
 
5.6%
york53
 
5.0%
brooklyn50
 
4.8%
beach48
 
4.6%
bronx39
 
3.7%
city20
 
1.9%
west18
 
1.7%
palm17
 
1.6%
lauderdale16
 
1.5%
Other values (322)667
63.4%

Most occurring characters

ValueCountFrequency (%)
a607
 
9.1%
e578
 
8.6%
o547
 
8.2%
r478
 
7.1%
i405
 
6.1%
n396
 
5.9%
l350
 
5.2%
328
 
4.9%
t281
 
4.2%
s218
 
3.3%
Other values (40)2498
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5306
79.4%
Uppercase Letter1052
 
15.7%
Space Separator328
 
4.9%

Most frequent character per category

ValueCountFrequency (%)
a607
11.4%
e578
10.9%
o547
10.3%
r478
 
9.0%
i405
 
7.6%
n396
 
7.5%
l350
 
6.6%
t281
 
5.3%
s218
 
4.1%
k198
 
3.7%
Other values (15)1248
23.5%
ValueCountFrequency (%)
B194
18.4%
M102
9.7%
N94
 
8.9%
P79
 
7.5%
H67
 
6.4%
C60
 
5.7%
Y58
 
5.5%
S55
 
5.2%
L53
 
5.0%
R43
 
4.1%
Other values (14)247
23.5%
ValueCountFrequency (%)
328
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6358
95.1%
Common328
 
4.9%

Most frequent character per script

ValueCountFrequency (%)
a607
 
9.5%
e578
 
9.1%
o547
 
8.6%
r478
 
7.5%
i405
 
6.4%
n396
 
6.2%
l350
 
5.5%
t281
 
4.4%
s218
 
3.4%
k198
 
3.1%
Other values (39)2300
36.2%
ValueCountFrequency (%)
328
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6686
100.0%

Most frequent character per block

ValueCountFrequency (%)
a607
 
9.1%
e578
 
8.6%
o547
 
8.2%
r478
 
7.1%
i405
 
6.1%
n396
 
5.9%
l350
 
5.2%
328
 
4.9%
t281
 
4.2%
s218
 
3.3%
Other values (40)2498
37.4%

State
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
NY
308 
FL
237 
NJ
177 
PA
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1448
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFL
2nd rowFL
3rd rowFL
4th rowFL
5th rowFL
ValueCountFrequency (%)
NY308
42.5%
FL237
32.7%
NJ177
24.4%
PA2
 
0.3%
2021-01-15T00:06:48.978701image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:06:49.135519image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
ny308
42.5%
fl237
32.7%
nj177
24.4%
pa2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
N485
33.5%
Y308
21.3%
F237
16.4%
L237
16.4%
J177
 
12.2%
P2
 
0.1%
A2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1448
100.0%

Most frequent character per category

ValueCountFrequency (%)
N485
33.5%
Y308
21.3%
F237
16.4%
L237
16.4%
J177
 
12.2%
P2
 
0.1%
A2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1448
100.0%

Most frequent character per script

ValueCountFrequency (%)
N485
33.5%
Y308
21.3%
F237
16.4%
L237
16.4%
J177
 
12.2%
P2
 
0.1%
A2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1448
100.0%

Most frequent character per block

ValueCountFrequency (%)
N485
33.5%
Y308
21.3%
F237
16.4%
L237
16.4%
J177
 
12.2%
P2
 
0.1%
A2
 
0.1%

County
Categorical

HIGH CORRELATION

Distinct26
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Miami Dade
105 
Broward
80 
Palm Beach
52 
New York
52 
Kings
50 
Other values (21)
385 

Length

Max length11
Median length7
Mean length7.364640884
Min length4

Characters and Unicode

Total characters5332
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBroward
2nd rowBroward
3rd rowBroward
4th rowPalm Beach
5th rowPalm Beach
ValueCountFrequency (%)
Miami Dade105
14.5%
Broward80
11.0%
Palm Beach52
 
7.2%
New York52
 
7.2%
Kings50
 
6.9%
Queens47
 
6.5%
Suffolk47
 
6.5%
Bronx39
 
5.4%
Nassau36
 
5.0%
Middlesex23
 
3.2%
Other values (16)193
26.7%
2021-01-15T00:06:49.637174image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
miami105
 
11.3%
dade105
 
11.3%
broward80
 
8.6%
new52
 
5.6%
beach52
 
5.6%
york52
 
5.6%
palm52
 
5.6%
kings50
 
5.4%
queens47
 
5.0%
suffolk47
 
5.0%
Other values (19)291
31.2%

Most occurring characters

ValueCountFrequency (%)
a514
 
9.6%
e509
 
9.5%
s340
 
6.4%
o337
 
6.3%
i336
 
6.3%
r335
 
6.3%
n270
 
5.1%
d265
 
5.0%
209
 
3.9%
m196
 
3.7%
Other values (25)2021
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4190
78.6%
Uppercase Letter933
 
17.5%
Space Separator209
 
3.9%

Most frequent character per category

ValueCountFrequency (%)
a514
12.3%
e509
12.1%
s340
 
8.1%
o337
 
8.0%
i336
 
8.0%
r335
 
8.0%
n270
 
6.4%
d265
 
6.3%
m196
 
4.7%
u179
 
4.3%
Other values (9)909
21.7%
ValueCountFrequency (%)
B190
20.4%
M167
17.9%
D105
11.3%
N88
9.4%
P67
 
7.2%
S58
 
6.2%
Y52
 
5.6%
K50
 
5.4%
Q47
 
5.0%
H20
 
2.1%
Other values (5)89
9.5%
ValueCountFrequency (%)
209
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5123
96.1%
Common209
 
3.9%

Most frequent character per script

ValueCountFrequency (%)
a514
 
10.0%
e509
 
9.9%
s340
 
6.6%
o337
 
6.6%
i336
 
6.6%
r335
 
6.5%
n270
 
5.3%
d265
 
5.2%
m196
 
3.8%
B190
 
3.7%
Other values (24)1831
35.7%
ValueCountFrequency (%)
209
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5332
100.0%

Most frequent character per block

ValueCountFrequency (%)
a514
 
9.6%
e509
 
9.5%
s340
 
6.4%
o337
 
6.3%
i336
 
6.3%
r335
 
6.3%
n270
 
5.1%
d265
 
5.0%
209
 
3.9%
m196
 
3.7%
Other values (25)2021
37.9%

Metro Area
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Nw Yrk, NY-NJ-PA
487 
Miami-Ft Ldr, FL
237 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters11584
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMiami-Ft Ldr, FL
2nd rowMiami-Ft Ldr, FL
3rd rowMiami-Ft Ldr, FL
4th rowMiami-Ft Ldr, FL
5th rowMiami-Ft Ldr, FL
ValueCountFrequency (%)
Nw Yrk, NY-NJ-PA487
67.3%
Miami-Ft Ldr, FL237
32.7%
2021-01-15T00:06:50.097162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:06:50.251034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
ny-nj-pa487
22.4%
yrk487
22.4%
nw487
22.4%
miami-ft237
10.9%
fl237
10.9%
ldr237
10.9%

Most occurring characters

ValueCountFrequency (%)
N1461
12.6%
1448
12.5%
-1211
 
10.5%
Y974
 
8.4%
r724
 
6.2%
,724
 
6.2%
w487
 
4.2%
k487
 
4.2%
J487
 
4.2%
P487
 
4.2%
Other values (9)3094
26.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5081
43.9%
Lowercase Letter3120
26.9%
Space Separator1448
 
12.5%
Dash Punctuation1211
 
10.5%
Other Punctuation724
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
N1461
28.8%
Y974
19.2%
J487
 
9.6%
P487
 
9.6%
A487
 
9.6%
F474
 
9.3%
L474
 
9.3%
M237
 
4.7%
ValueCountFrequency (%)
r724
23.2%
w487
15.6%
k487
15.6%
i474
15.2%
a237
 
7.6%
m237
 
7.6%
t237
 
7.6%
d237
 
7.6%
ValueCountFrequency (%)
-1211
100.0%
ValueCountFrequency (%)
1448
100.0%
ValueCountFrequency (%)
,724
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8201
70.8%
Common3383
29.2%

Most frequent character per script

ValueCountFrequency (%)
N1461
17.8%
Y974
11.9%
r724
8.8%
w487
 
5.9%
k487
 
5.9%
J487
 
5.9%
P487
 
5.9%
A487
 
5.9%
i474
 
5.8%
F474
 
5.8%
Other values (6)1659
20.2%
ValueCountFrequency (%)
1448
42.8%
-1211
35.8%
,724
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII11584
100.0%

Most frequent character per block

ValueCountFrequency (%)
N1461
12.6%
1448
12.5%
-1211
 
10.5%
Y974
 
8.4%
r724
 
6.2%
,724
 
6.2%
w487
 
4.2%
k487
 
4.2%
J487
 
4.2%
P487
 
4.2%
Other values (9)3094
26.7%

Neighborhood
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct349
Distinct (%)81.4%
Missing295
Missing (%)40.7%
Memory size5.8 KiB
Theater District
 
5
Central Harlem
 
4
East Harlem
 
4
Midtown East
 
4
Garment District
 
4
Other values (344)
408 

Length

Max length39
Median length12
Mean length12.62470862
Min length4

Characters and Unicode

Total characters5416
Distinct characters54
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique291 ?
Unique (%)67.8%

Sample

1st rowSleepy River
2nd rowWeston
3rd rowBaywinds
4th rowGolden Lakes
5th rowShoppes At Cresthaven
ValueCountFrequency (%)
Theater District5
 
0.7%
Central Harlem4
 
0.6%
East Harlem4
 
0.6%
Midtown East4
 
0.6%
Garment District4
 
0.6%
Valley Stream3
 
0.4%
Canarsie3
 
0.4%
Astoria3
 
0.4%
Bedford-Stuyvesant3
 
0.4%
Cutler Bay3
 
0.4%
Other values (339)393
54.3%
(Missing)295
40.7%
2021-01-15T00:06:50.818317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
east29
 
3.7%
park25
 
3.2%
heights21
 
2.6%
west19
 
2.4%
north15
 
1.9%
bay14
 
1.8%
district14
 
1.8%
miami13
 
1.6%
south13
 
1.6%
center11
 
1.4%
Other values (372)619
78.1%

Most occurring characters

ValueCountFrequency (%)
e502
 
9.3%
a433
 
8.0%
r367
 
6.8%
t367
 
6.8%
364
 
6.7%
o342
 
6.3%
i323
 
6.0%
n297
 
5.5%
s286
 
5.3%
l259
 
4.8%
Other values (44)1876
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4238
78.2%
Uppercase Letter799
 
14.8%
Space Separator364
 
6.7%
Dash Punctuation8
 
0.1%
Decimal Number6
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e502
11.8%
a433
10.2%
r367
8.7%
t367
8.7%
o342
 
8.1%
i323
 
7.6%
n297
 
7.0%
s286
 
6.7%
l259
 
6.1%
h146
 
3.4%
Other values (15)916
21.6%
ValueCountFrequency (%)
C92
 
11.5%
S73
 
9.1%
B63
 
7.9%
H61
 
7.6%
P60
 
7.5%
W47
 
5.9%
E44
 
5.5%
M43
 
5.4%
G40
 
5.0%
F35
 
4.4%
Other values (14)241
30.2%
ValueCountFrequency (%)
44
66.7%
12
33.3%
ValueCountFrequency (%)
364
100.0%
ValueCountFrequency (%)
-8
100.0%
ValueCountFrequency (%)
.1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5037
93.0%
Common379
 
7.0%

Most frequent character per script

ValueCountFrequency (%)
e502
 
10.0%
a433
 
8.6%
r367
 
7.3%
t367
 
7.3%
o342
 
6.8%
i323
 
6.4%
n297
 
5.9%
s286
 
5.7%
l259
 
5.1%
h146
 
2.9%
Other values (39)1715
34.0%
ValueCountFrequency (%)
364
96.0%
-8
 
2.1%
44
 
1.1%
12
 
0.5%
.1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5416
100.0%

Most frequent character per block

ValueCountFrequency (%)
e502
 
9.3%
a433
 
8.0%
r367
 
6.8%
t367
 
6.8%
364
 
6.7%
o342
 
6.3%
i323
 
6.0%
n297
 
5.5%
s286
 
5.3%
l259
 
4.8%
Other values (44)1876
34.6%

ZIP Code
Real number (ℝ≥0)

Distinct505
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17499.58564
Minimum7001
Maximum33498
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-01-15T00:06:51.102052image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum7001
5-th percentile7093.15
Q110001
median11236.5
Q333067.25
95-th percentile33413.85
Maximum33498
Range26497
Interquartile range (IQR)23066.25

Descriptive statistics

Standard deviation11068.42725
Coefficient of variation (CV)0.6324965335
Kurtosis-1.461343008
Mean17499.58564
Median Absolute Deviation (MAD)3361.5
Skewness0.6826024982
Sum12669700
Variance122510081.8
MonotocityNot monotonic
2021-01-15T00:06:51.392315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100015
 
0.7%
330125
 
0.7%
334144
 
0.6%
331264
 
0.6%
331574
 
0.6%
333514
 
0.6%
100364
 
0.6%
104674
 
0.6%
87534
 
0.6%
334114
 
0.6%
Other values (495)682
94.2%
ValueCountFrequency (%)
70011
0.1%
70022
0.3%
70032
0.3%
70041
0.1%
70051
0.1%
ValueCountFrequency (%)
334981
0.1%
334961
0.1%
334861
0.1%
334841
0.1%
334831
0.1%

ZIP Four
Real number (ℝ≥0)

MISSING

Distinct611
Distinct (%)90.1%
Missing46
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean3676.365782
Minimum1001
Maximum9847
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-01-15T00:06:51.670280image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1207.7
Q12111.5
median3320
Q34914.75
95-th percentile7200.45
Maximum9847
Range8846
Interquartile range (IQR)2803.25

Descriptive statistics

Standard deviation1899.1994
Coefficient of variation (CV)0.5165969635
Kurtosis0.0960931031
Mean3676.365782
Median Absolute Deviation (MAD)1319
Skewness0.7559688044
Sum2492576
Variance3606958.359
MonotocityNot monotonic
2021-01-15T00:06:51.948349image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13023
 
0.4%
32013
 
0.4%
21013
 
0.4%
20113
 
0.4%
44013
 
0.4%
60023
 
0.4%
48293
 
0.4%
19062
 
0.3%
47092
 
0.3%
24012
 
0.3%
Other values (601)651
89.9%
(Missing)46
 
6.4%
ValueCountFrequency (%)
10011
0.1%
10031
0.1%
10042
0.3%
10061
0.1%
10101
0.1%
ValueCountFrequency (%)
98471
0.1%
97841
0.1%
95481
0.1%
95011
0.1%
94941
0.1%

Year Established
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct15
Distinct (%)71.4%
Missing703
Missing (%)97.1%
Infinite0
Infinite (%)0.0%
Mean2004
Minimum1975
Maximum2019
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-01-15T00:06:52.207109image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1975
5-th percentile1989
Q11999
median2004
Q32015
95-th percentile2017
Maximum2019
Range44
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.9270307
Coefficient of variation (CV)0.005452610132
Kurtosis1.056861639
Mean2004
Median Absolute Deviation (MAD)6
Skewness-0.8493504161
Sum42084
Variance119.4
MonotocityNot monotonic
2021-01-15T00:06:52.420553image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
19892
 
0.3%
20032
 
0.3%
20082
 
0.3%
20152
 
0.3%
20042
 
0.3%
20162
 
0.3%
20071
 
0.1%
20191
 
0.1%
19981
 
0.1%
20171
 
0.1%
Other values (5)5
 
0.7%
(Missing)703
97.1%
ValueCountFrequency (%)
19751
0.1%
19892
0.3%
19971
0.1%
19981
0.1%
19991
0.1%
ValueCountFrequency (%)
20191
0.1%
20171
0.1%
20162
0.3%
20152
0.3%
20082
0.3%

Years In Database
Real number (ℝ≥0)

HIGH CORRELATION

Distinct37
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.45165746
Minimum1
Maximum37
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-01-15T00:06:52.651419image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.15
Q115.75
median26
Q335
95-th percentile37
Maximum37
Range36
Interquartile range (IQR)19.25

Descriptive statistics

Standard deviation10.12571791
Coefficient of variation (CV)0.4141117194
Kurtosis-1.077516639
Mean24.45165746
Median Absolute Deviation (MAD)10
Skewness-0.2935490153
Sum17703
Variance102.5301632
MonotocityNot monotonic
2021-01-15T00:06:52.913498image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
37152
21.0%
1242
 
5.8%
2839
 
5.4%
2430
 
4.1%
1329
 
4.0%
1426
 
3.6%
3625
 
3.5%
2524
 
3.3%
2624
 
3.3%
2723
 
3.2%
Other values (27)310
42.8%
ValueCountFrequency (%)
14
0.6%
25
0.7%
31
 
0.1%
45
0.7%
55
0.7%
ValueCountFrequency (%)
37152
21.0%
3625
 
3.5%
3510
 
1.4%
3414
 
1.9%
338
 
1.1%

Company Name
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Mc Donald's
712 
Mc Donald's Corporate Office
 
7
Mc Donald Shipper Air Freight
 
1
Mcdonald's Academy
 
1
Mc Donald's Bbq
 
1
Other values (2)
 
2

Length

Max length29
Median length11
Mean length11.21685083
Min length10

Characters and Unicode

Total characters8121
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.7%

Sample

1st rowMc Donald's
2nd rowMc Donald's
3rd rowMc Donald's
4th rowMc Donald's
5th rowMc Donald's
ValueCountFrequency (%)
Mc Donald's712
98.3%
Mc Donald's Corporate Office7
 
1.0%
Mc Donald Shipper Air Freight1
 
0.1%
Mcdonald's Academy1
 
0.1%
Mc Donald's Bbq1
 
0.1%
Mcdonalds Restaurants1
 
0.1%
Mc Donalds1
 
0.1%
2021-01-15T00:06:53.688597image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:06:53.873399image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
mc722
49.2%
donald's720
49.1%
corporate7
 
0.5%
office7
 
0.5%
bbq1
 
0.1%
donalds1
 
0.1%
restaurants1
 
0.1%
mcdonalds1
 
0.1%
academy1
 
0.1%
freight1
 
0.1%
Other values (4)4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
742
9.1%
o738
9.1%
a734
9.0%
c732
9.0%
d727
9.0%
n725
8.9%
s725
8.9%
M724
8.9%
l724
8.9%
D722
8.9%
Other values (21)828
10.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5192
63.9%
Uppercase Letter1466
 
18.1%
Space Separator742
 
9.1%
Other Punctuation721
 
8.9%

Most frequent character per category

ValueCountFrequency (%)
o738
14.2%
a734
14.1%
c732
14.1%
d727
14.0%
n725
14.0%
s725
14.0%
l724
13.9%
r18
 
0.3%
e18
 
0.3%
f14
 
0.3%
Other values (10)37
 
0.7%
ValueCountFrequency (%)
M724
49.4%
D722
49.2%
C7
 
0.5%
O7
 
0.5%
A2
 
0.1%
R1
 
0.1%
B1
 
0.1%
S1
 
0.1%
F1
 
0.1%
ValueCountFrequency (%)
742
100.0%
ValueCountFrequency (%)
'721
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6658
82.0%
Common1463
 
18.0%

Most frequent character per script

ValueCountFrequency (%)
o738
11.1%
a734
11.0%
c732
11.0%
d727
10.9%
n725
10.9%
s725
10.9%
M724
10.9%
l724
10.9%
D722
10.8%
r18
 
0.3%
Other values (19)89
 
1.3%
ValueCountFrequency (%)
742
50.7%
'721
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII8121
100.0%

Most frequent character per block

ValueCountFrequency (%)
742
9.1%
o738
9.1%
a734
9.0%
c732
9.0%
d727
9.0%
n725
8.9%
s725
8.9%
M724
8.9%
l724
8.9%
D722
8.9%
Other values (21)828
10.2%

Computer Expenses
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing2
Missing (%)0.3%
Memory size5.8 KiB
$2,500 to $5,000
482 
$5,000 to $10,000
136 
$1,000 to $2,500
90 
$500 to $1,000
 
7
Less than $500
 
7

Length

Max length17
Median length16
Mean length16.14958449
Min length14

Characters and Unicode

Total characters11660
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$1,000 to $2,500
2nd row$2,500 to $5,000
3rd row$2,500 to $5,000
4th row$2,500 to $5,000
5th row$2,500 to $5,000
ValueCountFrequency (%)
$2,500 to $5,000482
66.6%
$5,000 to $10,000136
 
18.8%
$1,000 to $2,50090
 
12.4%
$500 to $1,0007
 
1.0%
Less than $5007
 
1.0%
(Missing)2
 
0.3%
2021-01-15T00:06:54.382324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:06:54.567677image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to715
33.0%
5,000618
28.5%
2,500572
26.4%
10,000136
 
6.3%
1,00097
 
4.5%
50014
 
0.6%
less7
 
0.3%
than7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
03861
33.1%
1444
 
12.4%
$1437
 
12.3%
,1423
 
12.2%
51204
 
10.3%
t722
 
6.2%
o715
 
6.1%
2572
 
4.9%
1233
 
2.0%
s14
 
0.1%
Other values (5)35
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5870
50.3%
Lowercase Letter1479
 
12.7%
Space Separator1444
 
12.4%
Currency Symbol1437
 
12.3%
Other Punctuation1423
 
12.2%
Uppercase Letter7
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t722
48.8%
o715
48.3%
s14
 
0.9%
e7
 
0.5%
h7
 
0.5%
a7
 
0.5%
n7
 
0.5%
ValueCountFrequency (%)
03861
65.8%
51204
 
20.5%
2572
 
9.7%
1233
 
4.0%
ValueCountFrequency (%)
$1437
100.0%
ValueCountFrequency (%)
,1423
100.0%
ValueCountFrequency (%)
1444
100.0%
ValueCountFrequency (%)
L7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10174
87.3%
Latin1486
 
12.7%

Most frequent character per script

ValueCountFrequency (%)
t722
48.6%
o715
48.1%
s14
 
0.9%
L7
 
0.5%
e7
 
0.5%
h7
 
0.5%
a7
 
0.5%
n7
 
0.5%
ValueCountFrequency (%)
03861
37.9%
1444
 
14.2%
$1437
 
14.1%
,1423
 
14.0%
51204
 
11.8%
2572
 
5.6%
1233
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11660
100.0%

Most frequent character per block

ValueCountFrequency (%)
03861
33.1%
1444
 
12.4%
$1437
 
12.3%
,1423
 
12.2%
51204
 
10.3%
t722
 
6.2%
o715
 
6.1%
2572
 
4.9%
1233
 
2.0%
s14
 
0.1%
Other values (5)35
 
0.3%

Contract Labor Expenses
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing2
Missing (%)0.3%
Memory size5.8 KiB
$1,000 to $10,000
538 
$10,000 to $50,000
177 
Less than $1,000
 
7

Length

Max length18
Median length17
Mean length17.23545706
Min length16

Characters and Unicode

Total characters12444
Distinct characters14
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$1,000 to $10,000
2nd row$1,000 to $10,000
3rd row$1,000 to $10,000
4th row$1,000 to $10,000
5th row$1,000 to $10,000
ValueCountFrequency (%)
$1,000 to $10,000538
74.3%
$10,000 to $50,000177
 
24.4%
Less than $1,0007
 
1.0%
(Missing)2
 
0.3%
2021-01-15T00:06:55.156094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:06:55.338572image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
10,000715
33.0%
to715
33.0%
1,000545
25.2%
50,000177
 
8.2%
less7
 
0.3%
than7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
05203
41.8%
1444
 
11.6%
$1437
 
11.5%
,1437
 
11.5%
11260
 
10.1%
t722
 
5.8%
o715
 
5.7%
5177
 
1.4%
s14
 
0.1%
L7
 
0.1%
Other values (4)28
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6640
53.4%
Lowercase Letter1479
 
11.9%
Space Separator1444
 
11.6%
Currency Symbol1437
 
11.5%
Other Punctuation1437
 
11.5%
Uppercase Letter7
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t722
48.8%
o715
48.3%
s14
 
0.9%
e7
 
0.5%
h7
 
0.5%
a7
 
0.5%
n7
 
0.5%
ValueCountFrequency (%)
05203
78.4%
11260
 
19.0%
5177
 
2.7%
ValueCountFrequency (%)
$1437
100.0%
ValueCountFrequency (%)
,1437
100.0%
ValueCountFrequency (%)
1444
100.0%
ValueCountFrequency (%)
L7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10958
88.1%
Latin1486
 
11.9%

Most frequent character per script

ValueCountFrequency (%)
t722
48.6%
o715
48.1%
s14
 
0.9%
L7
 
0.5%
e7
 
0.5%
h7
 
0.5%
a7
 
0.5%
n7
 
0.5%
ValueCountFrequency (%)
05203
47.5%
1444
 
13.2%
$1437
 
13.1%
,1437
 
13.1%
11260
 
11.5%
5177
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII12444
100.0%

Most frequent character per block

ValueCountFrequency (%)
05203
41.8%
1444
 
11.6%
$1437
 
11.5%
,1437
 
11.5%
11260
 
10.1%
t722
 
5.8%
o715
 
5.7%
5177
 
1.4%
s14
 
0.1%
L7
 
0.1%
Other values (4)28
 
0.2%

Corporate Sales Volume Actual
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
$0
724 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1448
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$0
2nd row$0
3rd row$0
4th row$0
5th row$0
ValueCountFrequency (%)
$0724
100.0%
2021-01-15T00:06:55.793283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:06:55.943274image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
0724
100.0%

Most occurring characters

ValueCountFrequency (%)
$724
50.0%
0724
50.0%

Most occurring categories

ValueCountFrequency (%)
Currency Symbol724
50.0%
Decimal Number724
50.0%

Most frequent character per category

ValueCountFrequency (%)
$724
100.0%
ValueCountFrequency (%)
0724
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1448
100.0%

Most frequent character per script

ValueCountFrequency (%)
$724
50.0%
0724
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1448
100.0%

Most frequent character per block

ValueCountFrequency (%)
$724
50.0%
0724
50.0%

Insurance Expenses
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing2
Missing (%)0.3%
Memory size5.8 KiB
$25,000 to $50,000
478 
$50,000 to $100,000
141 
$10,000 to $25,000
88 
$2,500 to $5,000
 
8
$5,000 to $10,000
 
7

Length

Max length19
Median length18
Mean length18.1634349
Min length16

Characters and Unicode

Total characters13114
Distinct characters9
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$10,000 to $25,000
2nd row$25,000 to $50,000
3rd row$25,000 to $50,000
4th row$25,000 to $50,000
5th row$25,000 to $50,000
ValueCountFrequency (%)
$25,000 to $50,000478
66.0%
$50,000 to $100,000141
 
19.5%
$10,000 to $25,00088
 
12.2%
$2,500 to $5,0008
 
1.1%
$5,000 to $10,0007
 
1.0%
(Missing)2
 
0.3%
2021-01-15T00:06:56.319075image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:06:56.489323image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to722
33.3%
50,000619
28.6%
25,000566
26.1%
100,000141
 
6.5%
10,00095
 
4.4%
5,00015
 
0.7%
2,5008
 
0.4%

Most occurring characters

ValueCountFrequency (%)
05320
40.6%
$1444
 
11.0%
,1444
 
11.0%
1444
 
11.0%
51208
 
9.2%
t722
 
5.5%
o722
 
5.5%
2574
 
4.4%
1236
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7338
56.0%
Currency Symbol1444
 
11.0%
Other Punctuation1444
 
11.0%
Space Separator1444
 
11.0%
Lowercase Letter1444
 
11.0%

Most frequent character per category

ValueCountFrequency (%)
05320
72.5%
51208
 
16.5%
2574
 
7.8%
1236
 
3.2%
ValueCountFrequency (%)
t722
50.0%
o722
50.0%
ValueCountFrequency (%)
$1444
100.0%
ValueCountFrequency (%)
,1444
100.0%
ValueCountFrequency (%)
1444
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11670
89.0%
Latin1444
 
11.0%

Most frequent character per script

ValueCountFrequency (%)
05320
45.6%
$1444
 
12.4%
,1444
 
12.4%
1444
 
12.4%
51208
 
10.4%
2574
 
4.9%
1236
 
2.0%
ValueCountFrequency (%)
t722
50.0%
o722
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13114
100.0%

Most frequent character per block

ValueCountFrequency (%)
05320
40.6%
$1444
 
11.0%
,1444
 
11.0%
1444
 
11.0%
51208
 
9.2%
t722
 
5.5%
o722
 
5.5%
2574
 
4.4%
1236
 
1.8%

Last Updated On
Real number (ℝ≥0)

Distinct8
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202009.692
Minimum201910
Maximum202012
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-01-15T00:06:56.836636image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum201910
5-th percentile202010
Q1202010
median202010
Q3202010
95-th percentile202010
Maximum202012
Range102
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.278720236
Coefficient of variation (CV)2.613102462 × 105
Kurtosis344.8292844
Mean202009.692
Median Absolute Deviation (MAD)0
Skewness-18.42166086
Sum146255017
Variance27.86488732
MonotocityNot monotonic
2021-01-15T00:06:57.044617image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
202010675
93.2%
20201121
 
2.9%
20201211
 
1.5%
2020067
 
1.0%
2020075
 
0.7%
2020023
 
0.4%
2019111
 
0.1%
2019101
 
0.1%
ValueCountFrequency (%)
2019101
 
0.1%
2019111
 
0.1%
2020023
0.4%
2020067
1.0%
2020075
0.7%
ValueCountFrequency (%)
20201211
 
1.5%
20201121
 
2.9%
202010675
93.2%
2020075
 
0.7%
2020067
 
1.0%

Legal Expenses
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing3
Missing (%)0.4%
Memory size5.8 KiB
$2,500 to $5,000
485 
$5,000 to $10,000
117 
$1,000 to $2,500
105 
$500 to $1,000
 
7
Less than $500
 
7

Length

Max length17
Median length16
Mean length16.12343967
Min length14

Characters and Unicode

Total characters11625
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$1,000 to $2,500
2nd row$2,500 to $5,000
3rd row$2,500 to $5,000
4th row$2,500 to $5,000
5th row$2,500 to $5,000
ValueCountFrequency (%)
$2,500 to $5,000485
67.0%
$5,000 to $10,000117
 
16.2%
$1,000 to $2,500105
 
14.5%
$500 to $1,0007
 
1.0%
Less than $5007
 
1.0%
(Missing)3
 
0.4%
2021-01-15T00:06:57.545197image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:06:57.723546image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to714
33.0%
5,000602
27.8%
2,500590
27.3%
10,000117
 
5.4%
1,000112
 
5.2%
50014
 
0.6%
less7
 
0.3%
than7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
03818
32.8%
1442
 
12.4%
$1435
 
12.3%
,1421
 
12.2%
51206
 
10.4%
t721
 
6.2%
o714
 
6.1%
2590
 
5.1%
1229
 
2.0%
s14
 
0.1%
Other values (5)35
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5843
50.3%
Lowercase Letter1477
 
12.7%
Space Separator1442
 
12.4%
Currency Symbol1435
 
12.3%
Other Punctuation1421
 
12.2%
Uppercase Letter7
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t721
48.8%
o714
48.3%
s14
 
0.9%
e7
 
0.5%
h7
 
0.5%
a7
 
0.5%
n7
 
0.5%
ValueCountFrequency (%)
03818
65.3%
51206
 
20.6%
2590
 
10.1%
1229
 
3.9%
ValueCountFrequency (%)
$1435
100.0%
ValueCountFrequency (%)
,1421
100.0%
ValueCountFrequency (%)
1442
100.0%
ValueCountFrequency (%)
L7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10141
87.2%
Latin1484
 
12.8%

Most frequent character per script

ValueCountFrequency (%)
t721
48.6%
o714
48.1%
s14
 
0.9%
L7
 
0.5%
e7
 
0.5%
h7
 
0.5%
a7
 
0.5%
n7
 
0.5%
ValueCountFrequency (%)
03818
37.6%
1442
 
14.2%
$1435
 
14.2%
,1421
 
14.0%
51206
 
11.9%
2590
 
5.8%
1229
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11625
100.0%

Most frequent character per block

ValueCountFrequency (%)
03818
32.8%
1442
 
12.4%
$1435
 
12.3%
,1421
 
12.2%
51206
 
10.4%
t721
 
6.2%
o714
 
6.1%
2590
 
5.1%
1229
 
2.0%
s14
 
0.1%
Other values (5)35
 
0.3%

Location Employee Size Actual
Real number (ℝ≥0)

Distinct75
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.74447514
Minimum0
Maximum175
Zeros1
Zeros (%)0.1%
Memory size5.8 KiB
2021-01-15T00:06:58.079334image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.15
Q137
median45
Q354
95-th percentile74.85
Maximum175
Range175
Interquartile range (IQR)17

Descriptive statistics

Standard deviation15.91774013
Coefficient of variation (CV)0.3479707676
Kurtosis6.227204145
Mean45.74447514
Median Absolute Deviation (MAD)9
Skewness0.8491330689
Sum33119
Variance253.3744508
MonotocityNot monotonic
2021-01-15T00:06:58.398946image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45151
20.9%
5063
 
8.7%
4044
 
6.1%
6042
 
5.8%
3034
 
4.7%
3532
 
4.4%
4222
 
3.0%
6521
 
2.9%
5520
 
2.8%
2516
 
2.2%
Other values (65)279
38.5%
ValueCountFrequency (%)
01
 
0.1%
21
 
0.1%
42
 
0.3%
56
0.8%
61
 
0.1%
ValueCountFrequency (%)
1751
 
0.1%
1002
0.3%
901
 
0.1%
861
 
0.1%
854
0.6%

Location Employee Size Range
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.8%
Missing1
Missing (%)0.1%
Memory size5.8 KiB
20 to 49
435 
50 to 99
260 
10 to 19
 
12
5 to 9
 
10
100 to 249
 
3

Length

Max length10
Median length8
Mean length7.972337483
Min length6

Characters and Unicode

Total characters5764
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20 to 49
2nd row20 to 49
3rd row50 to 99
4th row20 to 49
5th row50 to 99
ValueCountFrequency (%)
20 to 49435
60.1%
50 to 99260
35.9%
10 to 1912
 
1.7%
5 to 910
 
1.4%
100 to 2493
 
0.4%
1 to 43
 
0.4%
(Missing)1
 
0.1%
2021-01-15T00:06:58.978960image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:06:59.165450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to723
33.3%
49435
20.1%
20435
20.1%
50260
 
12.0%
99260
 
12.0%
1912
 
0.6%
1012
 
0.6%
910
 
0.5%
510
 
0.5%
13
 
0.1%
Other values (3)9
 
0.4%

Most occurring characters

ValueCountFrequency (%)
1446
25.1%
9980
17.0%
t723
12.5%
o723
12.5%
0713
12.4%
4441
 
7.7%
2438
 
7.6%
5270
 
4.7%
130
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2872
49.8%
Space Separator1446
25.1%
Lowercase Letter1446
25.1%

Most frequent character per category

ValueCountFrequency (%)
9980
34.1%
0713
24.8%
4441
15.4%
2438
15.3%
5270
 
9.4%
130
 
1.0%
ValueCountFrequency (%)
t723
50.0%
o723
50.0%
ValueCountFrequency (%)
1446
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4318
74.9%
Latin1446
 
25.1%

Most frequent character per script

ValueCountFrequency (%)
1446
33.5%
9980
22.7%
0713
16.5%
4441
 
10.2%
2438
 
10.1%
5270
 
6.3%
130
 
0.7%
ValueCountFrequency (%)
t723
50.0%
o723
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5764
100.0%

Most frequent character per block

ValueCountFrequency (%)
1446
25.1%
9980
17.0%
t723
12.5%
o723
12.5%
0713
12.4%
4441
 
7.7%
2438
 
7.6%
5270
 
4.7%
130
 
0.5%

Location Sales Volume Actual
Categorical

HIGH CARDINALITY

Distinct344
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
$4,188,000
 
42
$2,337,000
 
21
$2,345,000
 
16
$3,713,000
 
14
$2,134,000
 
12
Other values (339)
619 

Length

Max length10
Median length10
Mean length9.91160221
Min length2

Characters and Unicode

Total characters7176
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique211 ?
Unique (%)29.1%

Sample

1st row$1,564,000
2nd row$2,554,000
3rd row$2,606,000
4th row$2,372,000
5th row$2,952,000
ValueCountFrequency (%)
$4,188,00042
 
5.8%
$2,337,00021
 
2.9%
$2,345,00016
 
2.2%
$3,713,00014
 
1.9%
$2,134,00012
 
1.7%
$2,606,00011
 
1.5%
$2,372,00011
 
1.5%
$2,031,00010
 
1.4%
$2,091,0009
 
1.2%
$2,324,0008
 
1.1%
Other values (334)570
78.7%
2021-01-15T00:06:59.755630image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,188,00042
 
5.8%
2,337,00021
 
2.9%
2,345,00016
 
2.2%
3,713,00014
 
1.9%
2,134,00012
 
1.7%
2,606,00011
 
1.5%
2,372,00011
 
1.5%
2,031,00010
 
1.4%
2,091,0009
 
1.2%
2,182,0008
 
1.1%
Other values (334)570
78.7%

Most occurring characters

ValueCountFrequency (%)
02343
32.7%
,1420
19.8%
$724
 
10.1%
2547
 
7.6%
1414
 
5.8%
3375
 
5.2%
8294
 
4.1%
4257
 
3.6%
6224
 
3.1%
7211
 
2.9%
Other values (2)367
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5032
70.1%
Other Punctuation1420
 
19.8%
Currency Symbol724
 
10.1%

Most frequent character per category

ValueCountFrequency (%)
02343
46.6%
2547
 
10.9%
1414
 
8.2%
3375
 
7.5%
8294
 
5.8%
4257
 
5.1%
6224
 
4.5%
7211
 
4.2%
5200
 
4.0%
9167
 
3.3%
ValueCountFrequency (%)
$724
100.0%
ValueCountFrequency (%)
,1420
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7176
100.0%

Most frequent character per script

ValueCountFrequency (%)
02343
32.7%
,1420
19.8%
$724
 
10.1%
2547
 
7.6%
1414
 
5.8%
3375
 
5.2%
8294
 
4.1%
4257
 
3.6%
6224
 
3.1%
7211
 
2.9%
Other values (2)367
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII7176
100.0%

Most frequent character per block

ValueCountFrequency (%)
02343
32.7%
,1420
19.8%
$724
 
10.1%
2547
 
7.6%
1414
 
5.8%
3375
 
5.2%
8294
 
4.1%
4257
 
3.6%
6224
 
3.1%
7211
 
2.9%
Other values (2)367
 
5.1%

Location Sales Volume Range
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.6%
Missing2
Missing (%)0.3%
Memory size5.8 KiB
$1-2.5 Million
370 
$2.5-5 Million
328 
$500,000-1 Million
 
13
Less Than $500,000
 
11

Length

Max length18
Median length14
Mean length14.13296399
Min length14

Characters and Unicode

Total characters10204
Distinct characters20
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$1-2.5 Million
2nd row$2.5-5 Million
3rd row$2.5-5 Million
4th row$1-2.5 Million
5th row$2.5-5 Million
ValueCountFrequency (%)
$1-2.5 Million370
51.1%
$2.5-5 Million328
45.3%
$500,000-1 Million13
 
1.8%
Less Than $500,00011
 
1.5%
(Missing)2
 
0.3%
2021-01-15T00:07:00.305210image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:07:00.507644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
million711
48.9%
1-2.5370
25.4%
2.5-5328
22.5%
500,000-113
 
0.9%
less11
 
0.8%
than11
 
0.8%
500,00011
 
0.8%

Most occurring characters

ValueCountFrequency (%)
i1422
13.9%
l1422
13.9%
51050
10.3%
733
7.2%
$722
7.1%
n722
7.1%
-711
7.0%
M711
7.0%
o711
7.0%
2698
6.8%
Other values (10)1302
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4332
42.5%
Decimal Number2251
22.1%
Space Separator733
 
7.2%
Uppercase Letter733
 
7.2%
Currency Symbol722
 
7.1%
Other Punctuation722
 
7.1%
Dash Punctuation711
 
7.0%

Most frequent character per category

ValueCountFrequency (%)
i1422
32.8%
l1422
32.8%
n722
16.7%
o711
16.4%
s22
 
0.5%
e11
 
0.3%
h11
 
0.3%
a11
 
0.3%
ValueCountFrequency (%)
51050
46.6%
2698
31.0%
1383
 
17.0%
0120
 
5.3%
ValueCountFrequency (%)
M711
97.0%
L11
 
1.5%
T11
 
1.5%
ValueCountFrequency (%)
.698
96.7%
,24
 
3.3%
ValueCountFrequency (%)
$722
100.0%
ValueCountFrequency (%)
-711
100.0%
ValueCountFrequency (%)
733
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5139
50.4%
Latin5065
49.6%

Most frequent character per script

ValueCountFrequency (%)
i1422
28.1%
l1422
28.1%
n722
14.3%
M711
14.0%
o711
14.0%
s22
 
0.4%
L11
 
0.2%
e11
 
0.2%
T11
 
0.2%
h11
 
0.2%
ValueCountFrequency (%)
51050
20.4%
733
14.3%
$722
14.0%
-711
13.8%
2698
13.6%
.698
13.6%
1383
 
7.5%
0120
 
2.3%
,24
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII10204
100.0%

Most frequent character per block

ValueCountFrequency (%)
i1422
13.9%
l1422
13.9%
51050
10.3%
733
7.2%
$722
7.1%
n722
7.1%
-711
7.0%
M711
7.0%
o711
7.0%
2698
6.8%
Other values (10)1302
12.8%

Management/Administration Expenses
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing2
Missing (%)0.3%
Memory size5.8 KiB
$10,000 to $25,000
560 
$25,000 to $50,000
89 
$5,000 to $10,000
57 
$2,500 to $5,000
 
8
Less than $2,500
 
8

Length

Max length18
Median length18
Mean length17.8767313
Min length16

Characters and Unicode

Total characters12907
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$10,000 to $25,000
2nd row$10,000 to $25,000
3rd row$10,000 to $25,000
4th row$10,000 to $25,000
5th row$10,000 to $25,000
ValueCountFrequency (%)
$10,000 to $25,000560
77.3%
$25,000 to $50,00089
 
12.3%
$5,000 to $10,00057
 
7.9%
$2,500 to $5,0008
 
1.1%
Less than $2,5008
 
1.1%
(Missing)2
 
0.3%
2021-01-15T00:07:01.018260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:07:01.206641image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to714
33.0%
25,000649
30.0%
10,000617
28.5%
50,00089
 
4.1%
5,00065
 
3.0%
2,50016
 
0.7%
less8
 
0.4%
than8
 
0.4%

Most occurring characters

ValueCountFrequency (%)
04998
38.7%
1444
 
11.2%
$1436
 
11.1%
,1436
 
11.1%
5819
 
6.3%
t722
 
5.6%
o714
 
5.5%
2665
 
5.2%
1617
 
4.8%
s16
 
0.1%
Other values (5)40
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7099
55.0%
Lowercase Letter1484
 
11.5%
Space Separator1444
 
11.2%
Currency Symbol1436
 
11.1%
Other Punctuation1436
 
11.1%
Uppercase Letter8
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t722
48.7%
o714
48.1%
s16
 
1.1%
e8
 
0.5%
h8
 
0.5%
a8
 
0.5%
n8
 
0.5%
ValueCountFrequency (%)
04998
70.4%
5819
 
11.5%
2665
 
9.4%
1617
 
8.7%
ValueCountFrequency (%)
$1436
100.0%
ValueCountFrequency (%)
,1436
100.0%
ValueCountFrequency (%)
1444
100.0%
ValueCountFrequency (%)
L8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11415
88.4%
Latin1492
 
11.6%

Most frequent character per script

ValueCountFrequency (%)
t722
48.4%
o714
47.9%
s16
 
1.1%
L8
 
0.5%
e8
 
0.5%
h8
 
0.5%
a8
 
0.5%
n8
 
0.5%
ValueCountFrequency (%)
04998
43.8%
1444
 
12.7%
$1436
 
12.6%
,1436
 
12.6%
5819
 
7.2%
2665
 
5.8%
1617
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII12907
100.0%

Most frequent character per block

ValueCountFrequency (%)
04998
38.7%
1444
 
11.2%
$1436
 
11.1%
,1436
 
11.1%
5819
 
6.3%
t722
 
5.6%
o714
 
5.5%
2665
 
5.2%
1617
 
4.8%
s16
 
0.1%
Other values (5)40
 
0.3%

Office Supplies Expense
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing2
Missing (%)0.3%
Memory size5.8 KiB
$20,000 to $50,000
381 
$50,000 to $100,000
317 
$10,000 to $20,000
 
14
$5,000 to $10,000
 
9
Less than $5,000
 
1

Length

Max length19
Median length18
Mean length18.42382271
Min length16

Characters and Unicode

Total characters13302
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row$20,000 to $50,000
2nd row$50,000 to $100,000
3rd row$50,000 to $100,000
4th row$20,000 to $50,000
5th row$50,000 to $100,000
ValueCountFrequency (%)
$20,000 to $50,000381
52.6%
$50,000 to $100,000317
43.8%
$10,000 to $20,00014
 
1.9%
$5,000 to $10,0009
 
1.2%
Less than $5,0001
 
0.1%
(Missing)2
 
0.3%
2021-01-15T00:07:01.884480image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:07:02.063164image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to721
33.3%
50,000698
32.2%
20,000395
18.2%
100,000317
14.6%
10,00023
 
1.1%
5,00010
 
0.5%
less1
 
< 0.1%
than1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
06079
45.7%
1444
 
10.9%
$1443
 
10.8%
,1443
 
10.8%
t722
 
5.4%
o721
 
5.4%
5708
 
5.3%
2395
 
3.0%
1340
 
2.6%
s2
 
< 0.1%
Other values (5)5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7522
56.5%
Lowercase Letter1449
 
10.9%
Space Separator1444
 
10.9%
Currency Symbol1443
 
10.8%
Other Punctuation1443
 
10.8%
Uppercase Letter1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
t722
49.8%
o721
49.8%
s2
 
0.1%
e1
 
0.1%
h1
 
0.1%
a1
 
0.1%
n1
 
0.1%
ValueCountFrequency (%)
06079
80.8%
5708
 
9.4%
2395
 
5.3%
1340
 
4.5%
ValueCountFrequency (%)
$1443
100.0%
ValueCountFrequency (%)
,1443
100.0%
ValueCountFrequency (%)
1444
100.0%
ValueCountFrequency (%)
L1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11852
89.1%
Latin1450
 
10.9%

Most frequent character per script

ValueCountFrequency (%)
t722
49.8%
o721
49.7%
s2
 
0.1%
L1
 
0.1%
e1
 
0.1%
h1
 
0.1%
a1
 
0.1%
n1
 
0.1%
ValueCountFrequency (%)
06079
51.3%
1444
 
12.2%
$1443
 
12.2%
,1443
 
12.2%
5708
 
6.0%
2395
 
3.3%
1340
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII13302
100.0%

Most frequent character per block

ValueCountFrequency (%)
06079
45.7%
1444
 
10.9%
$1443
 
10.8%
,1443
 
10.8%
t722
 
5.4%
o721
 
5.4%
5708
 
5.3%
2395
 
3.0%
1340
 
2.6%
s2
 
< 0.1%
Other values (5)5
 
< 0.1%

Own or Lease
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.9%
Missing379
Missing (%)52.3%
Memory size5.8 KiB
Unknown
127 
Own
126 
Lease
92 

Length

Max length7
Median length5
Mean length5.005797101
Min length3

Characters and Unicode

Total characters1727
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOwn
2nd rowUnknown
3rd rowOwn
4th rowOwn
5th rowOwn
ValueCountFrequency (%)
Unknown127
 
17.5%
Own126
 
17.4%
Lease92
 
12.7%
(Missing)379
52.3%
2021-01-15T00:07:02.696681image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:07:02.881546image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
unknown127
36.8%
own126
36.5%
lease92
26.7%

Most occurring characters

ValueCountFrequency (%)
n507
29.4%
w253
14.6%
e184
 
10.7%
U127
 
7.4%
k127
 
7.4%
o127
 
7.4%
O126
 
7.3%
L92
 
5.3%
a92
 
5.3%
s92
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1382
80.0%
Uppercase Letter345
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
n507
36.7%
w253
18.3%
e184
 
13.3%
k127
 
9.2%
o127
 
9.2%
a92
 
6.7%
s92
 
6.7%
ValueCountFrequency (%)
U127
36.8%
O126
36.5%
L92
26.7%

Most occurring scripts

ValueCountFrequency (%)
Latin1727
100.0%

Most frequent character per script

ValueCountFrequency (%)
n507
29.4%
w253
14.6%
e184
 
10.7%
U127
 
7.4%
k127
 
7.4%
o127
 
7.4%
O126
 
7.3%
L92
 
5.3%
a92
 
5.3%
s92
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1727
100.0%

Most frequent character per block

ValueCountFrequency (%)
n507
29.4%
w253
14.6%
e184
 
10.7%
U127
 
7.4%
k127
 
7.4%
o127
 
7.4%
O126
 
7.3%
L92
 
5.3%
a92
 
5.3%
s92
 
5.3%

Package Container Expense
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing2
Missing (%)0.3%
Memory size5.8 KiB
$10,000 to $25,000
450 
$25,000 to $50,000
242 
$5,000 to $10,000
 
19
$1,000 to $5,000
 
10
Less than $500
 
1

Length

Max length18
Median length18
Mean length17.94044321
Min length14

Characters and Unicode

Total characters12953
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row$10,000 to $25,000
2nd row$10,000 to $25,000
3rd row$10,000 to $25,000
4th row$10,000 to $25,000
5th row$25,000 to $50,000
ValueCountFrequency (%)
$10,000 to $25,000450
62.2%
$25,000 to $50,000242
33.4%
$5,000 to $10,00019
 
2.6%
$1,000 to $5,00010
 
1.4%
Less than $5001
 
0.1%
(Missing)2
 
0.3%
2021-01-15T00:07:03.309389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:07:03.797765image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to721
33.3%
25,000692
31.9%
10,000469
21.7%
50,000242
 
11.2%
5,00029
 
1.3%
1,00010
 
0.5%
less1
 
< 0.1%
5001
 
< 0.1%
than1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
05039
38.9%
1444
 
11.1%
$1443
 
11.1%
,1442
 
11.1%
5964
 
7.4%
t722
 
5.6%
o721
 
5.6%
2692
 
5.3%
1479
 
3.7%
s2
 
< 0.1%
Other values (5)5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7174
55.4%
Lowercase Letter1449
 
11.2%
Space Separator1444
 
11.1%
Currency Symbol1443
 
11.1%
Other Punctuation1442
 
11.1%
Uppercase Letter1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
t722
49.8%
o721
49.8%
s2
 
0.1%
e1
 
0.1%
h1
 
0.1%
a1
 
0.1%
n1
 
0.1%
ValueCountFrequency (%)
05039
70.2%
5964
 
13.4%
2692
 
9.6%
1479
 
6.7%
ValueCountFrequency (%)
$1443
100.0%
ValueCountFrequency (%)
,1442
100.0%
ValueCountFrequency (%)
1444
100.0%
ValueCountFrequency (%)
L1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11503
88.8%
Latin1450
 
11.2%

Most frequent character per script

ValueCountFrequency (%)
t722
49.8%
o721
49.7%
s2
 
0.1%
L1
 
0.1%
e1
 
0.1%
h1
 
0.1%
a1
 
0.1%
n1
 
0.1%
ValueCountFrequency (%)
05039
43.8%
1444
 
12.6%
$1443
 
12.5%
,1442
 
12.5%
5964
 
8.4%
2692
 
6.0%
1479
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII12953
100.0%

Most frequent character per block

ValueCountFrequency (%)
05039
38.9%
1444
 
11.1%
$1443
 
11.1%
,1442
 
11.1%
5964
 
7.4%
t722
 
5.6%
o721
 
5.6%
2692
 
5.3%
1479
 
3.7%
s2
 
< 0.1%
Other values (5)5
 
< 0.1%

Payroll and Benefits Expenses
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing2
Missing (%)0.3%
Memory size5.8 KiB
$500,000 to $1 Million
484 
$250,000 to $500,000
114 
$1 to $2.5 Million
106 
Less than $100,000
 
9
$100,000 to $250,000
 
9

Length

Max length22
Median length22
Mean length21.02216066
Min length18

Characters and Unicode

Total characters15178
Distinct characters19
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$250,000 to $500,000
2nd row$500,000 to $1 Million
3rd row$500,000 to $1 Million
4th row$500,000 to $1 Million
5th row$500,000 to $1 Million
ValueCountFrequency (%)
$500,000 to $1 Million484
66.9%
$250,000 to $500,000114
 
15.7%
$1 to $2.5 Million106
 
14.6%
Less than $100,0009
 
1.2%
$100,000 to $250,0009
 
1.2%
(Missing)2
 
0.3%
2021-01-15T00:07:04.396972image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:07:04.580607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to713
25.9%
500,000598
21.7%
million590
21.4%
1590
21.4%
250,000123
 
4.5%
2.5106
 
3.8%
100,00018
 
0.7%
than9
 
0.3%
less9
 
0.3%

Most occurring characters

ValueCountFrequency (%)
03572
23.5%
2034
13.4%
$1435
9.5%
o1303
 
8.6%
i1180
 
7.8%
l1180
 
7.8%
5827
 
5.4%
,739
 
4.9%
t722
 
4.8%
1608
 
4.0%
Other values (9)1578
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5236
34.5%
Lowercase Letter5029
33.1%
Space Separator2034
 
13.4%
Currency Symbol1435
 
9.5%
Other Punctuation845
 
5.6%
Uppercase Letter599
 
3.9%

Most frequent character per category

ValueCountFrequency (%)
o1303
25.9%
i1180
23.5%
l1180
23.5%
t722
14.4%
n599
11.9%
s18
 
0.4%
e9
 
0.2%
h9
 
0.2%
a9
 
0.2%
ValueCountFrequency (%)
03572
68.2%
5827
 
15.8%
1608
 
11.6%
2229
 
4.4%
ValueCountFrequency (%)
,739
87.5%
.106
 
12.5%
ValueCountFrequency (%)
M590
98.5%
L9
 
1.5%
ValueCountFrequency (%)
$1435
100.0%
ValueCountFrequency (%)
2034
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common9550
62.9%
Latin5628
37.1%

Most frequent character per script

ValueCountFrequency (%)
o1303
23.2%
i1180
21.0%
l1180
21.0%
t722
12.8%
n599
10.6%
M590
10.5%
s18
 
0.3%
L9
 
0.2%
e9
 
0.2%
h9
 
0.2%
ValueCountFrequency (%)
03572
37.4%
2034
21.3%
$1435
15.0%
5827
 
8.7%
,739
 
7.7%
1608
 
6.4%
2229
 
2.4%
.106
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15178
100.0%

Most frequent character per block

ValueCountFrequency (%)
03572
23.5%
2034
13.4%
$1435
9.5%
o1303
 
8.6%
i1180
 
7.8%
l1180
 
7.8%
5827
 
5.4%
,739
 
4.9%
t722
 
4.8%
1608
 
4.0%
Other values (9)1578
10.4%

Purchase Print Expenses
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.6%
Missing3
Missing (%)0.4%
Memory size5.8 KiB
$1,000 to $2,500
357 
$2,500 to $5,000
340 
$500 to $1,000
 
14
Less than $500
 
10

Length

Max length16
Median length16
Mean length15.9334258
Min length14

Characters and Unicode

Total characters11488
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$1,000 to $2,500
2nd row$2,500 to $5,000
3rd row$2,500 to $5,000
4th row$1,000 to $2,500
5th row$2,500 to $5,000
ValueCountFrequency (%)
$1,000 to $2,500357
49.3%
$2,500 to $5,000340
47.0%
$500 to $1,00014
 
1.9%
Less than $50010
 
1.4%
(Missing)3
 
0.4%
2021-01-15T00:07:05.222347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:07:05.461099image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to711
32.9%
2,500697
32.2%
1,000371
17.2%
5,000340
15.7%
50024
 
1.1%
less10
 
0.5%
than10
 
0.5%

Most occurring characters

ValueCountFrequency (%)
03575
31.1%
1442
12.6%
$1432
12.5%
,1408
 
12.3%
51061
 
9.2%
t721
 
6.3%
o711
 
6.2%
2697
 
6.1%
1371
 
3.2%
s20
 
0.2%
Other values (5)50
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5704
49.7%
Lowercase Letter1492
 
13.0%
Space Separator1442
 
12.6%
Currency Symbol1432
 
12.5%
Other Punctuation1408
 
12.3%
Uppercase Letter10
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t721
48.3%
o711
47.7%
s20
 
1.3%
e10
 
0.7%
h10
 
0.7%
a10
 
0.7%
n10
 
0.7%
ValueCountFrequency (%)
03575
62.7%
51061
 
18.6%
2697
 
12.2%
1371
 
6.5%
ValueCountFrequency (%)
$1432
100.0%
ValueCountFrequency (%)
,1408
100.0%
ValueCountFrequency (%)
1442
100.0%
ValueCountFrequency (%)
L10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common9986
86.9%
Latin1502
 
13.1%

Most frequent character per script

ValueCountFrequency (%)
t721
48.0%
o711
47.3%
s20
 
1.3%
L10
 
0.7%
e10
 
0.7%
h10
 
0.7%
a10
 
0.7%
n10
 
0.7%
ValueCountFrequency (%)
03575
35.8%
1442
14.4%
$1432
14.3%
,1408
 
14.1%
51061
 
10.6%
2697
 
7.0%
1371
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII11488
100.0%

Most frequent character per block

ValueCountFrequency (%)
03575
31.1%
1442
12.6%
$1432
12.5%
,1408
 
12.3%
51061
 
9.2%
t721
 
6.3%
o711
 
6.2%
2697
 
6.1%
1371
 
3.2%
s20
 
0.2%
Other values (5)50
 
0.4%

Rent Expenses
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing2
Missing (%)0.3%
Memory size5.8 KiB
$10,000 to $25,000
327 
$100,000 to $250,000
229 
$50,000 to $100,000
58 
Less than $10,000
56 
$25,000 to $50,000
52 

Length

Max length20
Median length18
Mean length18.63711911
Min length17

Characters and Unicode

Total characters13456
Distinct characters15
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$10,000 to $25,000
2nd row$10,000 to $25,000
3rd row$100,000 to $250,000
4th row$10,000 to $25,000
5th row$10,000 to $25,000
ValueCountFrequency (%)
$10,000 to $25,000327
45.2%
$100,000 to $250,000229
31.6%
$50,000 to $100,00058
 
8.0%
Less than $10,00056
 
7.7%
$25,000 to $50,00052
 
7.2%
(Missing)2
 
0.3%
2021-01-15T00:07:06.001407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:07:06.203752image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to666
30.7%
10,000383
17.7%
25,000379
17.5%
100,000287
13.3%
250,000229
 
10.6%
50,000110
 
5.1%
less56
 
2.6%
than56
 
2.6%

Most occurring characters

ValueCountFrequency (%)
05460
40.6%
1444
 
10.7%
$1388
 
10.3%
,1388
 
10.3%
t722
 
5.4%
5718
 
5.3%
1670
 
5.0%
o666
 
4.9%
2608
 
4.5%
s112
 
0.8%
Other values (5)280
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7456
55.4%
Lowercase Letter1724
 
12.8%
Space Separator1444
 
10.7%
Currency Symbol1388
 
10.3%
Other Punctuation1388
 
10.3%
Uppercase Letter56
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
t722
41.9%
o666
38.6%
s112
 
6.5%
e56
 
3.2%
h56
 
3.2%
a56
 
3.2%
n56
 
3.2%
ValueCountFrequency (%)
05460
73.2%
5718
 
9.6%
1670
 
9.0%
2608
 
8.2%
ValueCountFrequency (%)
$1388
100.0%
ValueCountFrequency (%)
,1388
100.0%
ValueCountFrequency (%)
1444
100.0%
ValueCountFrequency (%)
L56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11676
86.8%
Latin1780
 
13.2%

Most frequent character per script

ValueCountFrequency (%)
t722
40.6%
o666
37.4%
s112
 
6.3%
L56
 
3.1%
e56
 
3.1%
h56
 
3.1%
a56
 
3.1%
n56
 
3.1%
ValueCountFrequency (%)
05460
46.8%
1444
 
12.4%
$1388
 
11.9%
,1388
 
11.9%
5718
 
6.1%
1670
 
5.7%
2608
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII13456
100.0%

Most frequent character per block

ValueCountFrequency (%)
05460
40.6%
1444
 
10.7%
$1388
 
10.3%
,1388
 
10.3%
t722
 
5.4%
5718
 
5.3%
1670
 
5.0%
o666
 
4.9%
2608
 
4.5%
s112
 
0.8%
Other values (5)280
 
2.1%

Square Footage
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2,500 - 4,999
692 
1 - 1,499
 
27
5,000 - 9,999
 
3
1,500 - 2,499
 
1
10,000 - 19,999
 
1

Length

Max length15
Median length13
Mean length12.85359116
Min length9

Characters and Unicode

Total characters9306
Distinct characters9
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row2,500 - 4,999
2nd row2,500 - 4,999
3rd row2,500 - 4,999
4th row2,500 - 4,999
5th row2,500 - 4,999
ValueCountFrequency (%)
2,500 - 4,999692
95.6%
1 - 1,49927
 
3.7%
5,000 - 9,9993
 
0.4%
1,500 - 2,4991
 
0.1%
10,000 - 19,9991
 
0.1%
2021-01-15T00:07:06.880172image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:07:07.058047image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
724
33.3%
4,999692
31.9%
2,500692
31.9%
1,49927
 
1.2%
127
 
1.2%
5,0003
 
0.1%
9,9993
 
0.1%
19,9991
 
< 0.1%
1,5001
 
< 0.1%
2,4991
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
92148
23.1%
1448
15.6%
,1421
15.3%
01399
15.0%
-724
 
7.8%
4720
 
7.7%
5696
 
7.5%
2693
 
7.4%
157
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5713
61.4%
Space Separator1448
 
15.6%
Other Punctuation1421
 
15.3%
Dash Punctuation724
 
7.8%

Most frequent character per category

ValueCountFrequency (%)
92148
37.6%
01399
24.5%
4720
 
12.6%
5696
 
12.2%
2693
 
12.1%
157
 
1.0%
ValueCountFrequency (%)
,1421
100.0%
ValueCountFrequency (%)
1448
100.0%
ValueCountFrequency (%)
-724
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common9306
100.0%

Most frequent character per script

ValueCountFrequency (%)
92148
23.1%
1448
15.6%
,1421
15.3%
01399
15.0%
-724
 
7.8%
4720
 
7.7%
5696
 
7.5%
2693
 
7.4%
157
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII9306
100.0%

Most frequent character per block

ValueCountFrequency (%)
92148
23.1%
1448
15.6%
,1421
15.3%
01399
15.0%
-724
 
7.8%
4720
 
7.7%
5696
 
7.5%
2693
 
7.4%
157
 
0.6%

Telcom Expenses
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing2
Missing (%)0.3%
Memory size5.8 KiB
$5,000 to $20,000
599 
$2,000 to $5,000
109 
Less than $2,000
 
14

Length

Max length17
Median length17
Mean length16.82963989
Min length16

Characters and Unicode

Total characters12151
Distinct characters14
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$2,000 to $5,000
2nd row$5,000 to $20,000
3rd row$5,000 to $20,000
4th row$5,000 to $20,000
5th row$5,000 to $20,000
ValueCountFrequency (%)
$5,000 to $20,000599
82.7%
$2,000 to $5,000109
 
15.1%
Less than $2,00014
 
1.9%
(Missing)2
 
0.3%
2021-01-15T00:07:07.552476image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:07:07.719275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
5,000708
32.7%
to708
32.7%
20,000599
27.7%
2,000123
 
5.7%
less14
 
0.6%
than14
 
0.6%

Most occurring characters

ValueCountFrequency (%)
04889
40.2%
1444
 
11.9%
$1430
 
11.8%
,1430
 
11.8%
2722
 
5.9%
t722
 
5.9%
o708
 
5.8%
5708
 
5.8%
s28
 
0.2%
L14
 
0.1%
Other values (4)56
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6319
52.0%
Lowercase Letter1514
 
12.5%
Space Separator1444
 
11.9%
Currency Symbol1430
 
11.8%
Other Punctuation1430
 
11.8%
Uppercase Letter14
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t722
47.7%
o708
46.8%
s28
 
1.8%
e14
 
0.9%
h14
 
0.9%
a14
 
0.9%
n14
 
0.9%
ValueCountFrequency (%)
04889
77.4%
2722
 
11.4%
5708
 
11.2%
ValueCountFrequency (%)
$1430
100.0%
ValueCountFrequency (%)
,1430
100.0%
ValueCountFrequency (%)
1444
100.0%
ValueCountFrequency (%)
L14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10623
87.4%
Latin1528
 
12.6%

Most frequent character per script

ValueCountFrequency (%)
t722
47.3%
o708
46.3%
s28
 
1.8%
L14
 
0.9%
e14
 
0.9%
h14
 
0.9%
a14
 
0.9%
n14
 
0.9%
ValueCountFrequency (%)
04889
46.0%
1444
 
13.6%
$1430
 
13.5%
,1430
 
13.5%
2722
 
6.8%
5708
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII12151
100.0%

Most frequent character per block

ValueCountFrequency (%)
04889
40.2%
1444
 
11.9%
$1430
 
11.8%
,1430
 
11.8%
2722
 
5.9%
t722
 
5.9%
o708
 
5.8%
5708
 
5.8%
s28
 
0.2%
L14
 
0.1%
Other values (4)56
 
0.5%

Utilities Expenses
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)1.0%
Missing2
Missing (%)0.3%
Memory size5.8 KiB
$2,000 to $5,000
317 
$50,000 to $100,000
178 
$5,000 to $10,000
62 
Over $100,000
52 
$25,000 to $50,000
42 
Other values (2)
71 

Length

Max length19
Median length16
Mean length16.81440443
Min length13

Characters and Unicode

Total characters12140
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$2,000 to $5,000
2nd row$2,000 to $5,000
3rd row$50,000 to $100,000
4th row$2,000 to $5,000
5th row$2,000 to $5,000
ValueCountFrequency (%)
$2,000 to $5,000317
43.8%
$50,000 to $100,000178
24.6%
$5,000 to $10,00062
 
8.6%
Over $100,00052
 
7.2%
$25,000 to $50,00042
 
5.8%
Less than $2,00039
 
5.4%
$10,000 to $25,00032
 
4.4%
(Missing)2
 
0.3%
2021-01-15T00:07:08.222767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-15T00:07:08.407524image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to631
29.8%
5,000379
17.9%
2,000356
16.8%
100,000230
 
10.9%
50,000220
 
10.4%
10,00094
 
4.4%
25,00074
 
3.5%
over52
 
2.5%
less39
 
1.8%
than39
 
1.8%

Most occurring characters

ValueCountFrequency (%)
04833
39.8%
1392
 
11.5%
$1353
 
11.1%
,1353
 
11.1%
5673
 
5.5%
t670
 
5.5%
o631
 
5.2%
2430
 
3.5%
1324
 
2.7%
e91
 
0.7%
Other values (8)390
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6260
51.6%
Lowercase Letter1691
 
13.9%
Space Separator1392
 
11.5%
Currency Symbol1353
 
11.1%
Other Punctuation1353
 
11.1%
Uppercase Letter91
 
0.7%

Most frequent character per category

ValueCountFrequency (%)
t670
39.6%
o631
37.3%
e91
 
5.4%
s78
 
4.6%
v52
 
3.1%
r52
 
3.1%
h39
 
2.3%
a39
 
2.3%
n39
 
2.3%
ValueCountFrequency (%)
04833
77.2%
5673
 
10.8%
2430
 
6.9%
1324
 
5.2%
ValueCountFrequency (%)
O52
57.1%
L39
42.9%
ValueCountFrequency (%)
$1353
100.0%
ValueCountFrequency (%)
,1353
100.0%
ValueCountFrequency (%)
1392
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10358
85.3%
Latin1782
 
14.7%

Most frequent character per script

ValueCountFrequency (%)
t670
37.6%
o631
35.4%
e91
 
5.1%
s78
 
4.4%
O52
 
2.9%
v52
 
2.9%
r52
 
2.9%
L39
 
2.2%
h39
 
2.2%
a39
 
2.2%
ValueCountFrequency (%)
04833
46.7%
1392
 
13.4%
$1353
 
13.1%
,1353
 
13.1%
5673
 
6.5%
2430
 
4.2%
1324
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII12140
100.0%

Most frequent character per block

ValueCountFrequency (%)
04833
39.8%
1392
 
11.5%
$1353
 
11.1%
,1353
 
11.1%
5673
 
5.5%
t670
 
5.5%
o631
 
5.2%
2430
 
3.5%
1324
 
2.7%
e91
 
0.7%
Other values (8)390
 
3.2%

Interactions

2021-01-15T00:06:31.483689image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:31.721405image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:31.893616image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:32.134592image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:32.367435image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:32.624738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:32.847268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:33.021844image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:33.257789image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:33.485404image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:33.736198image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:33.912901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:34.093369image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:34.284831image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:34.459712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:34.792394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:35.044152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:35.285889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:35.481799image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:35.734636image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:35.999491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:36.232914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:36.468130image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:36.648276image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:36.894368image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:37.157156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:37.416155image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:37.673168image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:37.867198image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-15T00:06:38.166743image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-01-15T00:07:08.813138image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-15T00:07:09.131629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-15T00:07:09.434615image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-15T00:07:09.840231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-01-15T00:07:10.594077image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-01-15T00:06:38.898310image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-01-15T00:06:42.119355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-01-15T00:06:43.295396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-01-15T00:06:44.764021image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Advertising ExpensesAccounting ExpensesAddressCityStateCountyMetro AreaNeighborhoodZIP CodeZIP FourYear EstablishedYears In DatabaseCompany NameComputer ExpensesContract Labor ExpensesCorporate Sales Volume ActualInsurance ExpensesLast Updated OnLegal ExpensesLocation Employee Size ActualLocation Employee Size RangeLocation Sales Volume ActualLocation Sales Volume RangeManagement/Administration ExpensesOffice Supplies ExpenseOwn or LeasePackage Container ExpensePayroll and Benefits ExpensesPurchase Print ExpensesRent ExpensesSquare FootageTelcom ExpensesUtilities Expenses
0$20,000 to $50,000$2,500 to $5,000265 W Oakland Park BlvdWilton ManorsFLBrowardMiami-Ft Ldr, FLSleepy River333111707.0NaN37Mc Donald's$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202010$1,000 to $2,5003020 to 49$1,564,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000Own$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$2,000 to $5,000$2,000 to $5,000
1$50,000 to $100,000$5,000 to $10,000326 Indian TrceWestonFLBrowardMiami-Ft Ldr, FLWeston333262996.0NaN24Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004920 to 49$2,554,000$2.5-5 Million$10,000 to $25,000$50,000 to $100,000Unknown$10,000 to $25,000$500,000 to $1 Million$2,500 to $5,000$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
2$50,000 to $100,000$5,000 to $10,0001020 Weston RdWestonFLBrowardMiami-Ft Ldr, FLNaN333261917.0NaN32Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0005050 to 99$2,606,000$2.5-5 Million$10,000 to $25,000$50,000 to $100,000Own$10,000 to $25,000$500,000 to $1 Million$2,500 to $5,000$100,000 to $250,0002,500 - 4,999$5,000 to $20,000$50,000 to $100,000
3$50,000 to $100,000$5,000 to $10,0009835 Okeechobee BlvdWest Palm BeachFLPalm BeachMiami-Ft Ldr, FLBaywinds334111833.0NaN13Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004520 to 49$2,372,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
4$50,000 to $100,000$5,000 to $10,000828 S Military TrlWest Palm BeachFLPalm BeachMiami-Ft Ldr, FLNaN334153908.0NaN28Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0005650 to 99$2,952,000$2.5-5 Million$10,000 to $25,000$50,000 to $100,000Own$25,000 to $50,000$500,000 to $1 Million$2,500 to $5,000$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
5$50,000 to $100,000$5,000 to $10,0006858 Okeechobee BlvdWest Palm BeachFLPalm BeachMiami-Ft Ldr, FLGolden Lakes334112510.0NaN30Mc Donald's$5,000 to $10,000$10,000 to $50,000$0$50,000 to $100,000202010$5,000 to $10,0006550 to 99$3,426,000$2.5-5 Million$10,000 to $25,000$50,000 to $100,000Own$25,000 to $50,000$500,000 to $1 Million$2,500 to $5,000$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
6$50,000 to $100,000$5,000 to $10,000650 Belvedere RdWest Palm BeachFLPalm BeachMiami-Ft Ldr, FLNaN334051231.0NaN36Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004020 to 49$2,109,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000Own$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500$100,000 to $250,0002,500 - 4,999$5,000 to $20,000$50,000 to $100,000
7$20,000 to $50,000$5,000 to $10,0004275 45th StWest Palm BeachFLPalm BeachMiami-Ft Ldr, FLNaN334071859.0NaN30Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0003520 to 49$1,845,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000Own$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
8$50,000 to $100,000$5,000 to $10,0003015 Forest Hill BlvdWest Palm BeachFLPalm BeachMiami-Ft Ldr, FLNaN334065908.0NaN4Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004520 to 49$2,372,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500Less than $10,0002,500 - 4,999$5,000 to $20,000Less than $2,000
9$50,000 to $100,000$5,000 to $10,0002605 S Military TrlWest Palm BeachFLPalm BeachMiami-Ft Ldr, FLShoppes At Cresthaven334157549.0NaN9Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004520 to 49$2,372,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500Less than $10,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000

Last rows

Advertising ExpensesAccounting ExpensesAddressCityStateCountyMetro AreaNeighborhoodZIP CodeZIP FourYear EstablishedYears In DatabaseCompany NameComputer ExpensesContract Labor ExpensesCorporate Sales Volume ActualInsurance ExpensesLast Updated OnLegal ExpensesLocation Employee Size ActualLocation Employee Size RangeLocation Sales Volume ActualLocation Sales Volume RangeManagement/Administration ExpensesOffice Supplies ExpenseOwn or LeasePackage Container ExpensePayroll and Benefits ExpensesPurchase Print ExpensesRent ExpensesSquare FootageTelcom ExpensesUtilities Expenses
714$20,000 to $50,000$2,500 to $5,0004174 White Plains RdBronxNYBronxNw Yrk, NY-NJ-PAWakefield104663012.0NaN25Mc Donald's$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202010$1,000 to $2,5002020 to 49$930,000$500,000-1 Million$5,000 to $10,000$10,000 to $20,000Own$5,000 to $10,000$250,000 to $500,000$500 to $1,000$50,000 to $100,0002,500 - 4,999$2,000 to $5,000$25,000 to $50,000
715$50,000 to $100,000$5,000 to $10,00051-67 161st StBronxNYBronxNw Yrk, NY-NJ-PANaN10451NaNNaN29Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0005950 to 99$2,742,000$2.5-5 Million$10,000 to $25,000$50,000 to $100,000Unknown$25,000 to $50,000$500,000 to $1 Million$2,500 to $5,000$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
716$50,000 to $100,000$5,000 to $10,0005765 BroadwayBronxNYBronxNw Yrk, NY-NJ-PAKingsbridge104634144.0NaN37Mc Donald's$2,500 to $5,000$10,000 to $50,000$0$25,000 to $50,000202010$2,500 to $5,0006550 to 99$3,021,000$2.5-5 Million$10,000 to $25,000$50,000 to $100,000Lease$25,000 to $50,000$500,000 to $1 Million$2,500 to $5,000$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
717$20,000 to $50,000$5,000 to $10,000597-99 Grand ConcourseBronxNYBronxNw Yrk, NY-NJ-PANaN10451NaNNaN12Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004020 to 49$1,859,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
718$20,000 to $50,000$5,000 to $10,000599 E Tremont AveBronxNYBronxNw Yrk, NY-NJ-PAEast Tremont104574727.0NaN14Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004020 to 49$1,859,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$5,000 to $20,000$2,000 to $5,000
719$50,000 to $100,000$5,000 to $10,000724 E 241st StBronxNYBronxNw Yrk, NY-NJ-PAWakefield104701302.0NaN20Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004520 to 49$2,091,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500$100,000 to $250,0002,500 - 4,999$5,000 to $20,000$50,000 to $100,000
720$20,000 to $50,000$2,500 to $5,000839 Westchester AveBronxNYBronxNw Yrk, NY-NJ-PAWoodstock104551704.0NaN18Mc Donald's$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202010$1,000 to $2,5003020 to 49$1,394,000$1-2.5 Million$5,000 to $10,000$20,000 to $50,000NaN$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500Less than $10,0002,500 - 4,999$2,000 to $5,000$2,000 to $5,000
721$50,000 to $100,000$5,000 to $10,00086 E 167th StBronxNYBronxNw Yrk, NY-NJ-PAConcourse104528203.0NaN16Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004520 to 49$2,091,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500Less than $10,0002,500 - 4,999$5,000 to $20,000Less than $2,000
722$20,000 to $50,000$2,500 to $5,000875 Garrison AveBronxNYBronxNw Yrk, NY-NJ-PAHunts Point104745305.0NaN31Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$1,000 to $2,5003520 to 49$1,627,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$2,000 to $5,000$2,000 to $5,000
723$50,000 to $100,000$5,000 to $10,000925 Hunts Point AveBronxNYBronxNw Yrk, NY-NJ-PANaN104595190.0NaN4Mc Donald's$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202010$2,500 to $5,0004520 to 49$2,091,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$500,000 to $1 Million$1,000 to $2,500Less than $10,0002,500 - 4,999$5,000 to $20,000$50,000 to $100,000